Information processing apparatus, image processing method and recording medium on which image processing program is recorded

ABSTRACT

An information processing apparatus, includes: a memory; and a processor coupled to the memory, wherein the processor: generates a sharpened image of a fingerprint image by emphasizing edges of a fingerprint pattern included in the fingerprint image; calculates an edge density of the sharpened image based on a local change of luminance; and decides based on the edge density whether each of pixels of the sharpened image is in a fingerprint region or a background region.

CROSS-REFERENCE TO RELATED APPLICATION

This application is based upon and claims the benefit of priority of theprior Japanese Patent Application No. 2018-700, filed on Jan. 5, 2018,the entire contents of which are incorporated herein by reference.

FIELD

The embodiment discussed herein relates to an information processingapparatus, an image processing method and a recording medium on which animage processing program is recorded.

BACKGROUND

According to a fingerprint authentication technology in which afingerprint is used when identification of an individual is performed,feature information for identifying an individual is extracted from afingerprint image acquired from a user.

Examples of the related art include Japanese Laid-open PatentPublication No. 2003-44856 and Japanese Laid-open Patent Publication No.2003-337949.

SUMMARY

According to an aspect of the embodiment, an information processingapparatus, includes: a memory; and a processor coupled to the memory,wherein the processor: generates a sharpened image of a fingerprintimage by emphasizing edges of a fingerprint pattern included in thefingerprint image; calculates an edge density of the sharpened imagebased on a local change of luminance; and decides based on the edgedensity whether each of pixels of the sharpened image is in afingerprint region or a background region.

The object and advantages of the invention will be realized and attainedby means of the elements and combinations particularly pointed out inthe claims.

It is to be understood that both the foregoing general description andthe following detailed description are exemplary and explanatory and arenot restrictive of the invention.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a block diagram depicting a hardware configuration of an imageprocessing apparatus according to an embodiment 1;

FIG. 2 is a block diagram of each of functions implemented by executionof an image processing program;

FIG. 3 is a flow chart exemplifying a registration process;

FIG. 4A is a view exemplifying a fingerprint image;

FIG. 4B is a view exemplifying a fingerprint image in which noise isincluded in a background region;

FIG. 5A is a view exemplifying luminance values in a fingerprint regionin a fingerprint image one-dimensionally along a horizontal direction ofan image;

FIG. 5B is a view exemplifying luminance values in a background region,in which noise is included, one-dimensionally along a horizontaldirection of an image;

FIG. 6A is a view exemplifying luminance values in a case wheresharpening is performed for luminance values in FIG. 5A;

FIG. 6B is a view exemplifying luminance values in a case wheresharpening is performed for luminance values in FIG. 5B;

FIG. 7A is a view exemplifying a response of a second order differentialfilter to FIG. 6A;

FIG. 7B is a view exemplifying a response of a second order differentialfilter to FIG. 6B;

FIG. 8 is a view exemplifying fingerprint region candidates andbackground region candidates;

FIG. 9 is a view exemplifying fingerprint region candidates andbackground region candidates;

FIG. 10 is a flow chart exemplifying details of an authenticationprocess; and

FIG. 11 is a view exemplifying an image processing system according to amodification.

DESCRIPTION OF EMBODIMENTS

For example, when feature information is to be extracted, a backgroundregion is reduced and only a fingerprint region is handled. However, ifthe background region of a fingerprint image includes noise, thedistinction between the fingerprint region and the background regionbecomes ambiguous and this increases erroneous detection of featurepoints, resulting in the possibility that the authentication accuracymay be deteriorated. For example, in the case where a sensor degradedwith a time-dependent variation, in the case where a less expensive andlow quality sensor is used or in a like case, it is conceivable toperform processing using such a low quality fingerprint image asincludes noise over a wide range of the background region. Therefore,for example, the fingerprint region and the background region areseparated from each other.

For example, it is sometimes presupposed that the fingerprint region andthe background region have therebetween a significant difference inaverage or variance of luminance values. However, in a fingerprint imagein which noise is included, ridges in the fingerprint region becomeambiguous, and this decreases the variance of luminance values.Meanwhile, since, in the background region, pixels of various luminancevalues are included, the variance of luminance values increases, andtherefore, the difference in variance of luminance values between thefingerprint region and the background region decreases. As result, thereis the possibility that it may become difficult to correctly separatethe fingerprint region and the background region from each other. Forexample, if contrast adjustment is performed for a noise region of thebackground, the contrast increases also in the noise region, and theremay be a case that the distributions of luminance values or luminancegradients in the fingerprint region and the noise region become similarto each other in comparison with that in the original image. As aresult, there is the possibility that distinction between thefingerprint region and the noise region may become difficult and thepossibility that noise in the background region may be detected as afingerprint feature point in error may increase.

For example, an information processing apparatus or the like may beprovided that separates a background region and a fingerprint regionwith a high degree of accuracy from a fingerprint image.

In the following, an embodiment is described with reference to thedrawings.

Embodiment 1

FIG. 1 is a block diagram depicting a hardware configuration of an imageprocessing apparatus according to an embodiment 1. As exemplified inFIG. 1, an image processing apparatus 100 includes a central processingunit (CPU) 101, a random access memory (RAM) 102, a storage apparatus103, a display apparatus 104, a biological sensor 105, a communicationunit 106, an attribute information acquisition unit 107 and so forth.The components mentioned are coupled to each other by a bus.

The CPU 101 is a central processing unit. The CPU 101 includes one ormore cores. The RAM 102 is a volatile memory for temporarily storingprograms to be executed by the CPU 101, data to be processed by the CPU101 and so forth.

The storage apparatus 103 is a nonvolatile storage apparatus. As thestorage apparatus 103, for example, a solid state drive (SSD) such as aread only memory (ROM) or a flash memory, a hard disk that is driven bya hard disk drive and so forth may be used. An image processing programaccording to the present embodiment is stored in the storage apparatus103. The display apparatus 104 is a liquid crystal display, anelectroluminescence panel or the like and displays a result of eachprocess hereinafter described or the like.

The biological sensor 105 is a sensor that acquires a fingerprint imageof a user. The biological sensor 105 is, for example, an optical sensor,a capacitance sensor or the like. The communication unit 106 is acoupling interface, for example, to a local area network (LAN) or thelike. The attribute information acquisition unit 107 is an inputtingapparatus such as a keyboard, a mouse and so forth and is an apparatusfor inputting attribute information for identifying a user such as anidentification (ID), a user name, a password and so forth.

The image processing program stored in the storage apparatus 103 isdeployed into the RAM 102 in a executable state. The CPU 101 executesthe image processing program deployed in the RAM 102. Consequently,various processes are executed by the image processing apparatus 100.When the image processing program is executed, a registration process,an authentication process and other processes are executed.

The registration process is a process for registering, associated withthe attribute information of each user, a biological feature obtainedfrom a fingerprint image acquired by the biological sensor 105 asregistration biological feature. In the present embodiment, afingerprint pattern and so forth extracted from a fingerprint image areregistered as biological features. The authentication process is aprocess for verifying a verification biological feature acquired by thebiological sensor 105 and registration biological features with eachother. In the present embodiment, as an example, if the similaritybetween a verification biological feature acquired at the time of anauthentication process and a registration biological feature registeredin advance is equal to or higher than a threshold value, it is decidedthat the user is the same person as the registered user. Details of theregistration process and the authentication process are hereinafterdescribed.

FIG. 2 is a block diagram of each function implemented by execution ofan image processing program. By execution of the image processingprogram, an image acquisition unit 10, a sharpening unit 20, an edgedensity calculation unit 30, a region decision unit 40, a separationunit 50, a noise reduction unit 60, a registration unit 70, a database80, an authentication unit 90 and so forth are implemented. It is to benoted that, although the apparatus of FIGS. 1 and 2 is depicted suchthat it is configured as a standalone terminal, the apparatus is notlimited to this. For example, the present embodiment may be applied alsoto a system of a client server. In the embodiment described below, forthe simplification of the description, an example of a terminal of thestandalone type is described.

(Registration Process)

FIG. 3 is a flow chart exemplifying details of a registration process.In the following, the registration process is described with referenceto FIGS. 1 to 3. First, the attribute information acquisition unit 107acquires attribute information of a user (step S1). Then, the imageacquisition unit 10 acquires a fingerprint image of the user from thebiological sensor 105 (step S2). For example, the biological sensor 105acquires a fingerprint image of a given finger of the user by that theuser places a finger on a sensor face or slidably moves a finger on thesensor face.

FIG. 4A is a view exemplifying a fingerprint image. In the example ofFIG. 4A, since the fingerprint image does not include noise, afingerprint region and a background region may be distinguished fromeach other clearly. However, there is the possibility that various noisemay be generated in the fingerprint image from various factors. Forexample, due to abnormality in sensitivity characteristic of thebiological sensor 105, noise may appear in the background region inwhich no finger touches. FIG. 4B is a view exemplifying a fingerprintimage in which noise is included in a background region. If such afingerprint image including noise as just described is used, anerroneous feature point is detected in the background region, and thisundesirably becomes a factor of false rejection and others acceptance.Therefore, the image processing apparatus 100 according to the presentembodiment separates the background region including noise and thefingerprint region accurately from each other to reduce noise.

The sharpening unit 20 applies a sharpening filter to the fingerprintimage acquired at step S2 to generate a sharpened fingerprint image(step S3). For example, the sharpening unit 20 applies a process foremphasizing edges to the fingerprint image to generate a sharpenedfingerprint image. As a method for emphasizing edges, for example, atechnique for convoluting such an unsharp mask as given by the followingexpression (1):

$\begin{matrix}\begin{pmatrix}{{- 1}/4} & {{- 1}/2} & {{- 1}/4} \\{{- 1}/2} & 4 & {{- 1}/2} \\{{- 1}/4} & {{- 1}/2} & {{- 1}/4}\end{pmatrix} & (1)\end{matrix}$

Since, in the fingerprint region, ridges exist in a regularly lined upstate toward a given direction, strong edges at which the luminancevalue indicates a steep variation and flat portions that are not theedges are distributed regularly along the ridges. On the other hand, inthe background region in which noise is included, weak edges thatindicate a gentle variation of the luminance value are distributedirregularly and a small number of flat portions exit. FIG. 5A is a viewexemplifying luminance values in a fingerprint region in a fingerprintimage one-dimensionally along a horizontal direction of an image. FIG.5B is a view exemplifying luminance values in a background region, inwhich noise is included, one-dimensionally along a horizontal directionof an image. In FIGS. 5A and 5B, the axis of abscissa indicates aposition in the horizontal direction. As exemplified in FIG. 5A, atfingerprint ridge portions, the luminance value indicates a value closeto 0, but at fingerprint valley portions or in the background region inwhich no fingerprint exists, the luminance value indicates a value closeto 255. As exemplified in FIG. 5B, in the case where noise exits, avalue close to 0 appears also in the background region.

FIG. 6A is a view exemplifying luminance values in a case wheresharpening is performed for luminance values in FIG. 5A. FIG. 6B is aview exemplifying luminance values in a case where sharpening isperformed for luminance values in FIG. 5B. Although, in an originalfingerprint image, a location at which an edge becomes ambiguous by aninfluence of a state of a finger surface, a manner of touch with thesensor or the like sometimes appears, by emphasizing edges by asharpening process, the influence is reduced, and consequently, the edgedetection accuracy by the second order differential filter may beenhanced. Meanwhile, also in the background region in which noise isincluded, since edges are emphasized by the sharpening process, thenoise increases. However, since a local change degree of the luminancediffers between the background region including the noise and thefingerprint region, the two regions may be distinguished from eachother.

Then, the edge density calculation unit 30 divides the sharpenedfingerprint image into a plurality of small regions and calculates, foreach of the plurality of small regions, the ratio of pixels of which thedegree of change of the luminance value with respect to peripheralpixels is equal to or higher than a threshold value and included in thesmall region as an edge density. For example, the edge densitycalculation unit 30 first applies a second order differential filtersuch as a Laplacian of Gaussian (LOG) filter to the sharpenedfingerprint image generated at step S3 (step S4). The LoG filter may berepresented, for example, like an expression (2) given below. Byconvolving the LoG filter for the sharpened image as represented by anexpression (3) given below, a response of the second order differentialfilter may be obtained. In the expression (3) given below, Ix representsthe sharpened fingerprint image. In the expression (2) given below, (x,y) represents coordinates on the sharpened fingerprint image, and arepresents the size of a window. It is to be noted that, as the degreeof change of the luminance value becomes steeper, the absolute value ofthe response of the second order differential filter increases.Accordingly, by calculating an absolute value of the response of thesecond order differential filter, it is possible to detect whether ornot a steep edge exists. FIG. 7A is a view exemplifying a response of asecond order differential filter to FIG. 6A. FIG. 7B is a viewexemplifying a response of a second order differential filter to FIG.6B. As exemplified by FIG. 7A, in the fingerprint region, a value havinga high absolute value appears periodically. As exemplified in FIG. 7B,in the background region in which noise is included, values having ahigh absolute value decrease. It is to be noted that, in FIGS. 7A and7B, inflection points in FIGS. 6A and 6B reduce to zero.

$\begin{matrix}{{{LoG}\left( {x,y} \right)} = {{\frac{- 1}{\pi\;\sigma^{4}}\left\lbrack {1 - \frac{x^{2} + y^{2}}{2\;\sigma^{2}}} \right\rbrack}e^{- \frac{x^{2} + y^{2}}{2\;\sigma^{2}}}}} & (2) \\{{LoG} \otimes {Ix}} & (3)\end{matrix}$

Then, the edge density calculation unit 30 divides the sharpenedfingerprint image into small regions and calculates an edge density bycalculating an existence ratio of pixels in regard to which the responseof the second order differential filter is equal to or higher than agiven value for each small region (step S5). For example, the edgedensity calculation unit 30 generates a plurality of small regions ofsquares of 8×8 pixels to divide the sharpened fingerprint image into thesmall regions. Then, the edge density calculation unit 30 calculates,for each small region, the number N of pixels in regard to which theabsolute value of the response of the second order differential filteris equal to or higher than a given threshold value Th1 and divides N bythe pixel number included in the small region to determine an edgedensity D.

In the fingerprint region, the value of the edge density D is high, butin the background region in which noise is included, the value of theedge density D is low. Therefore, the region decision unit 40 decidesany small region in which the edge density D is equal to or higher thana given threshold value Th2 as a fingerprint region candidate while itdecides any other region as a background region candidate to classifythe small regions into fingerprint region candidates and backgroundregion candidates (step S6).

Incidentally, a calculated fingerprint region candidate or a backgroundregion candidate is sometimes decided in error by an influence of aforeign article attached to the sensor face, a scratch on the fingersurface or the like. In this case, sets of the fingerprint regioncandidates and sets of the background region candidates sometimespresent such a figure that has holes with enclaves as exemplified inFIG. 8. It is to be noted that, in FIG. 8, each white square indicates abackground region candidate while each shadowed square indicates afingerprint region candidate. However, each of an actual fingerprintregion and an actual background region does not include any enclave andis a hole-free region. Therefore, the separation unit 50 performs anexpansion process and a contraction process of morphology operation forthe sets of the fingerprint region candidates and the sets of thebackground region candidates to deform the candidates to generate onefingerprint region and one background region having no enclave (stepS7).

According to the expansion process, an operation for deciding, in thecase where one or more fingerprint region candidates exist in smallregions neighboring (4 neighbor or 8 neighbor) with a small regiondecided as a background region candidate, the small region decided as abackground region candidate newly as a fingerprint region candidate isperformed for all small regions decided as the background regioncandidates. According to the contraction process, an operation fordeciding, in the case where one or more background region candidatesexist in small regions neighboring (4 neighbor or 8 neighbor) with asmall region decided as a fingerprint region candidate, the small regiondecided as a fingerprint region candidate newly as a background regioncandidate is performed for all small regions decided as the fingerprintregion candidates. By performing the expansion process by i times andperforming the contraction process by i times in this order, thebackground region candidates existing as enclaves in the fingerprintregion candidates may be reduced. By performing the contraction processby j times and performing the expansion process by j times in thisorder, the fingerprint region candidates existing as enclaves in thebackground region candidates may be reduced. By the processes, afingerprint region candidate and a background region candidate having noenclave are generated as exemplified in FIG. 9 and are set as afingerprint region and a background region, respectively.

Then, the noise reduction unit 60 sets the luminance value of each ofthe pixels included in the background region to a background luminancevalue equal to or higher than a given value thereby to obtain afingerprint image in which background noise is reduced (step S8). Thebackground luminance value is, in the present embodiment, 255 as anexample.

Then, the registration unit 70 extracts a biological feature from thefingerprint image generated at step S8 and registers the biologicalfeature as a registration biological feature in an associatedrelationship with the attribute information acquired at step S1 into thedatabase 80 (step S9). The registration process ends therewith.

(Authentication Process)

FIG. 10 is a flow chart exemplifying details of an authenticationprocess executed in a case when a user performs authentication after aregistration process. As exemplified in FIG. 10, processes similar tothose at steps S1 to S8 are executed at steps S11 to S18, respectively.Then, the authentication unit 90 extracts a fingerprint feature as averification biological feature from the fingerprint image after thenoise reduction. Then, the authentication unit 90 reads out theregistration biological features associated with the attributeinformation acquired at step S11 from the database 80 and verifies theverification biological feature with the registration biologicalfeatures (step S19).

For example, the authentication unit 90 calculates a similarity betweenthe verification biological feature and the registration biologicalfeatures. The similarity represents that, as the value thereofincreases, the similarity of the biological features to each otherincreases, and, for example, where the biological features arerepresented by feature vectors, the similarity is the reciprocal of aEuclidean distance between them. For example, the authentication unit 90performs identification decision by a threshold value process of thesimilarity. For example, in the case where the similarity is equal to orhigher than a given threshold value, the authentication unit 90 decidesthat the registered person and the person to be verified are the sameperson but decides, in the case where the verification score is lowerthan the given threshold value, the two persons are different from eachother. It is to be noted that, without executing step S11, 1:Nauthentication may be performed by verifying the verification biologicalfeature with an unspecified large number of registration biologicalfeatures and performing identification by a threshold value process ofthe highest similarity.

According to the present embodiment, by emphasizing edges of afingerprint pattern included in a fingerprint image, a sharpened imageof the fingerprint image may be generated. Consequently, edges of afingerprint pattern may be detected with high accuracy. Then, from thesharpened image, an edge density may be calculated based on a localchange of the luminance. Since the edge density is high in a fingerprintregion, by calculating the edge density, each of pixels may beclassified into those of a fingerprint region and a background regionwith high accuracy.

Preferably, the sharpened image is divided into a plurality of smallregions and, in each of the small regions, the ratio of pixels in regardto each of which the degree of change of the luminance value from thatof a peripheral pixel is equal to or higher than a threshold value fromamong the pixels included in the small region is calculated as the edgedensity. In this case, the calculation accuracy of the edge density isenhanced.

More preferably, a second order differential filter is applied to thesharpened image to divide the sharpened image into a small regions and,for each of the plurality of small regions, the ratio of pixels inregard to which the absolute value of the response of the second orderfilter is equal to or higher than a threshold value from among thepixels included in the small region is calculated as the edge density.In this case, the calculation accuracy of the edge density is furtherenhanced.

By deforming the sharpened image such that sets of fingerprint regioncandidates and sets of background region candidates individually becomefigures having no hole therein, the sharpened image may be separatedinto a fingerprint region and a background region. By setting theluminance values of the separated background region to luminance valuesequal to or higher than the given value, noise in the background regionmay be reduced.

(Modification)

FIG. 11 is a view exemplifying an image processing system according to amodification. In the examples described above, the components of FIG. 2acquire a biological image from the biological sensor 105, acquiresattribute information from the attribute information acquisition unit107 and performs a registration process and an authentication process.As an alternative, a server 202 having the function of the components ofFIG. 2 may acquire a biological image from the biological sensor 105 andacquire attribute information from the attribute information acquisitionunit 107 through an electric communication line 201 such as theInternet.

In the example described above, the sharpening unit 20 functions as anexample of a generation unit that emphasizes edges of a fingerprintpattern included in a fingerprint image to generate a sharpened image ofthe fingerprint image. The edge density calculation unit 30 functions asan example of a calculation unit that calculates an edge density basedon a local change of the luminance from the sharpened image. The regiondecision unit 40 and the separation unit 50 function as an example of adecision unit that decides based on the edge density whether each pixelof the sharpened image is in a fingerprint region or a backgroundregion. The separation unit 50 functions as an example of a separationunit that separates the sharpened image into a finger region and abackground region by deforming the sharpened image such that sets offingerprint region candidates and sets of background region candidatesindividually become figures having no hole therein. The noise reductionunit 60 functions as an example of a noise reduction unit that reducesnoise in the background region by setting the luminance values in thebackground region separated by the separation unit to luminance valuesequal to or higher than a given value.

While the embodiment of the present technology is described in detail,the technology is not limited to the specific embodiment and may bemodified and altered in various manners without departing from thesubject matter of the technology set forth in the claims.

All examples and conditional language provided herein are intended forthe pedagogical purposes of aiding the reader in understanding theinvention and the concepts contributed by the inventor to further theart, and are not to be construed as limitations to such specificallyrecited examples and conditions, nor does the organization of suchexamples in the specification relate to a showing of the superiority andinferiority of the invention. Although one or more embodiments of thepresent invention have been described in detail, it should be understoodthat the various changes, substitutions, and alterations could be madehereto without departing from the spirit and scope of the invention.

What is claimed is:
 1. An information processing apparatus, comprising:a memory; and a processor coupled to the memory, wherein the processor:generates, by emphasizing edges, which correspond to changes ofluminance of a fingerprint pattern included in a fingerprint image, asharpened image of the fingerprint image; applies a second orderdifferential filter to the sharpened image; divides the sharpened imageinto a plurality of small regions; and calculates, for each of theplurality of small regions, a ratio of pixels in regard to which anabsolute value of a response of the second order differential filter isequal to or higher than a threshold value from among pixels included inthe small region as the edge density; and decides based on the edgedensity whether each of pixels of the sharpened image is in afingerprint region or a background region.
 2. The information processingapparatus according to claim 1, wherein the processor divides thesharpened image into a plurality of small regions and calculates, foreach of the plurality of small regions, a ratio of pixels each of whicha degree of change of a luminance value from a luminance value of aperipheral pixel is equal to or higher than a threshold value from amongpixels included in the small region as the edge density.
 3. Theinformation processing apparatus according to claim 1, wherein thesecond order differential filter is a Laplacian of Gaussian filter. 4.The information processing apparatus according to claim 1, wherein theprocessor classifies each of pixels of the sharpened image into afingerprint region candidate or a background region candidate inresponse to the edge density; and deforms the sharpened image such thatsets of fingerprint region candidates and sets of background regioncandidates individually become figures having no hole therein toseparate the sharpened image into the fingerprint region and thebackground region.
 5. The information processing apparatus according toclaim 4, wherein the processor applies an expansion process and acontraction process of morphology operation to the sets of thefingerprint region candidates and the sets of the background regioncandidates to deform the sharpened image such that the fingerprintregion and the background region individually form one figure in thesharpened image.
 6. The information processing apparatus according toclaim 1, wherein noise in the background region is reduced by settingluminance values in the background region decided by the processor toluminance values equal to or higher than a given value.
 7. Theinformation processing apparatus according to claim 6, wherein theprocessor extracts a verification biological feature in the image inwhich the noise is reduced; and verifies a registration biologicalfeature registered in advance and the verification biological featurewith each other to perform individual authentication.
 8. An imageprocessing method, comprising: generating, by a computer, by emphasizingedges, which correspond to changes of luminance of a fingerprint patternincluded in a fingerprint image, a sharpened image of the fingerprintimage; applying a second order differential filter to the sharpenedimage; dividing the sharpened image into a plurality of small regions;and calculating, for each of the plurality of small regions, a ratio ofpixels in regard to which an absolute value of a response of the secondorder differential filter is equal to or higher than a threshold valuefrom among pixels included in the small region as the edge density; anddeciding based on the edge density whether each of pixels of thesharpened image is in a fingerprint region or a background region. 9.The image processing method according to claim 8, further comprising:dividing the sharpened image into a plurality of small regions, andcalculating, for each of the plurality of small regions, a ratio ofpixels each of which a degree of change of a luminance value from aluminance value of a peripheral pixel is equal to or higher than athreshold value from among pixels included in the small region as theedge density.
 10. The image processing method according to claim 8,wherein the second order differential filter is a Laplacian of Gaussianfilter.
 11. The image processing method according to claim 8, furthercomprising: classifying each of pixels of the sharpened image into afingerprint region candidate or a background region candidate inresponse to the edge density; and deforming the sharpened image suchthat sets of fingerprint region candidates and sets of background regioncandidates individually become figures having no hole therein toseparate the sharpened image into the fingerprint region and thebackground region.
 12. The image processing method according to claim11, further comprising: applying an expansion process and a contractionprocess of morphology operation to the sets of the fingerprint regioncandidates and the sets of the background region candidates to deformthe sharpened image such that the fingerprint region and the backgroundregion individually form one figure in the sharpened image.
 13. Theimage processing method according to claim 8, wherein noise in thebackground region is reduced by setting luminance values in thebackground region decided by the processor to luminance values equal toor higher than a given value.
 14. The image processing method accordingto claim 13, further comprising: extracting a verification biologicalfeature in the image in which the noise is reduced; and verifying aregistration biological feature registered in advance and theverification biological feature with each other to perform individualauthentication.
 15. A non-transitory computer-readable medium recordingan image processing program which causes a computer to perform aprocess, the process comprising: generating, by emphasizing edges, whichcorrespond to changes of luminance of a fingerprint pattern included ina fingerprint image, a sharpened image of the fingerprint image;applying a second order differential filter to the sharpened image;dividing the sharpened image into a plurality of small regions; andcalculating, for each of the plurality of small regions, a ratio ofpixels in regard to which an absolute value of a response of the secondorder differential filter is equal to or higher than a threshold valuefrom among pixels included in the small region as the edge density; anddeciding based on the edge density whether each of pixels of thesharpened image is in a fingerprint region or a background region. 16.The non-transitory computer-readable medium according to claim 15,further comprising: dividing the sharpened image into a plurality ofsmall regions, and calculating, for each of the plurality of smallregions, a ratio of pixels each of which a degree of change of aluminance value from a luminance value of a peripheral pixel is equal toor higher than a threshold value from among pixels included in the smallregion as the edge density.
 17. The non-transitory computer-readablemedium according to claim 15, further comprising: classifying each ofpixels of the sharpened image into a fingerprint region candidate or abackground region candidate in response to the edge density; anddeforming the sharpened image such that sets of fingerprint regioncandidates and sets of background region candidates individually becomefigures having no hole therein to separate the sharpened image into thefingerprint region and the background region.